What Is Business-Centric Data Science?

Within the business enterprise, data science serves the same purpose that business intelligence does — to convert raw data into business insights that business leaders and managers can use to make data-informed decisions.

If you have large sets of structured and unstructured data sources that may or may not be complete and you want to convert those sources into valuable insights for decision support across the enterprise, call on a data scientist. Business-centric data science is multi-disciplinary and incorporates the ­following elements:

Quantitative analysis: Can be in the form of mathematical modeling, multivariate statistical analysis, forecasting, and/or simulations.

The term multivariate refers to more than one variable. A multivariate statistical analysis is a simultaneous statistical analysis of more than one variable at a time.

Programming skills: You need the necessary programming skills to both analyze raw data and make this data accessible to business users.

Business knowledge: You need knowledge of the business and its environment so that you can better understand the relevancy of your findings.

Data science is a pioneering discipline. Data scientists often employ the scientific method for data exploration, hypotheses formation, and hypothesis testing (through simulation and statistical modeling). Business-centric data scientists generate valuable data insights, oftentimes by exploring patterns and anomalies in business data. Data science in a business context is commonly comprised of

Internal and external datasets: Data science is flexible. You can create business data mash-ups from internal and external sources of structured and unstructured data fairly easily. (A data mash-up is combination of two or more data sources that are then analyzed together in order to provide users with a more complete view of the situation at hand.)

Like business analysts, business-centric data scientists produce decision-support products for business managers and organizational leaders to use. These products include analytics dashboards and data visualizations, but generally not tabular data reports and tables.

Data useful in business-centric data science

You can use data science to derive business insights from standard-sized sets of structured business data (just like BI) or from structured, semi-structured, and unstructured sets of big data. Data science solutions are not confined to transactional data that sits in a relational database; you can use data science to create valuable insights from all available data sources. These data sources include

Transactional business data: A tried-and-true data source, transactional business data is the type of structured data used in traditional BI and includes management data, customer service data, sales and marketing data, operational data, and employee performance data.

Social data related to the brand or business: A more recent phenomenon, the data covered by this rubric includes the unstructured data generated through emails, instant messaging, and social networks such as Twitter, Facebook, LinkedIn, Pinterest, and Instagram.

The acronym SCADA refers to Supervisory Control and Data Acquisition. SCADA systems are used to control remotely operating mechanical systems and equipment. They generate data that is used to monitor the operations of machines and equipment.

Audio, video, image, and PDF file data: These well-established formats are all sources of unstructured data.

Technologies and skillsets useful in business-centric data science

Since the products of data science are often generated from big data, cloud-based data platform solutions are common in the field. Data that’s used in data science is often derived from data-engineered big data solutions, like Hadoop, MapReduce, and Massively Parallel Processing.

Data scientists are innovative, forward-thinkers who must often think outside-the-box in order to exact solutions to the problems they solve. Many data scientists tend toward open-source solutions when available. From a cost perspective, this approach benefits the organizations that employ these scientists.

Business-centric data scientists might use machine learning techniques to find patterns in (and derive insights from) huge datasets that are related to a line of business or the business at large. They’re skilled in math, statistics, and programming, and they sometimes use these skills to generate predictive models.

They generally know how to program in Python or R. Most of them know how to use SQL to query relevant data from structured databases. They are usually skilled at communicating data insights to end users — in business-centric data science, end users are business managers and organizational leaders. Data scientists must be skillful at using verbal, oral, and visual means to communicate valuable data insights.

Although business-centric data scientists serve a decision-support role in the enterprise, they’re different from the business analyst in that they usually have strong academic and professional backgrounds in math, science, engineering, or all of the above. This said, business-centric data scientists also have a strong substantive knowledge of business management.